Unlocking the Power of Health Data and Facilitating Interoperability

The healthcare industry cannot afford to lag behind in an age where data drives decisions. Our comprehensive article dives into the critical role of healthcare data interoperability in enhancing patient care, advancing research, and tackling global health challenges. From the rise of FHIR as an industry standard to the barriers hindering seamless data exchange, get the insights you need to navigate the complexities of healthcare data.

The healthcare industry generates vast volumes of data daily, from patient records to medical research. Therefore, enabling the interoperable exchange of healthcare data is one of the industry’s most critical and challenging tasks.

Healthcare data interoperability refers to the ability of different systems to exchange data seamlessly.  The correct interpretation and seamless exchange of health data enable stakeholders to access necessary health knowledge across multiple organizations so the caregivers can see the complete picture of the patient and ensure the best care quality. Moreover, it guarantees simplified access to data and, more importantly, its accurate interpretation during medical research and clinical trials. 

Interoperable medical records ensure safe and accurate treatment of traveling patients by providing local physicians access to crucial patient health data, such as medical history, allergies, etc. Also, researchers can make essential insights and discoveries from big data in healthcare

Healthcare stakeholders face significant barriers to interoperability, including technical challenges, privacy and security concerns, the lack of standardization across healthcare organizations, and more. To facilitate healthcare interoperability, stakeholders must address these challenges and stick to effective data exchange strategies to ensure the accessibility and safety of sensitive data. 

FHIR (Fast Healthcare Interoperability Resources) has proven its extraordinary capacity as the industry-standard data format. FHIR promotes collaboration between industry stakeholders. By addressing the data exchange challenges and implementing robust sharing policies, stakeholders unlock the potential of health data and deliver better care to patients. 


Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore

Precision medicine (PM) refers to tailoring disease treatment and prevention, taking into account a patient’s unique genetic makeup and environmental factors, to deliver the most personalized treatment possible. Precision medicine collects and analyzes vast amounts of genetic and other patient data. 

In the article on the future of big data in healthcare, we shared the example of using big genomic data for personalized healthcare. By adapting treatments for the unique characteristics and needs of each patient, healthcare professionals can reduce adverse events and keep healthcare costs under control. 

For instance, in cancer diagnosis, the tumor sequence can help identify genetic mutations and abnormalities to target the specific defects driving cancer. However, to fully use the potential of PM and identify patterns for informed treatment decisions, healthcare stakeholders must ensure healthcare interoperability of the genomic, clinical, and lifestyle data of the patients. 

The research on the priorities and preferences of Singaporeans for sharing health-related data for PM in Singapore highlights the importance of public attitudes toward data sharing for precision medicine. The study found that most respondents were willing to share their de-identified health data for IRB-approved research without re-consenting to each survey. Moreover, government agencies and public institutions were the most trusted data users.

The study also revealed that consent was the least important attribute for sharing data. These findings suggest that the social license for data sharing in Singapore currently supports linking health and genomic data and sharing with public institutions for health research and quality improvement but does not support sharing with private health insurers or for private commercial use.

Strengthening Global Public Health Surveillance through Data and Benefit Sharing

Disease surveillance (DS) is a component of global public health that allows for early warning of outbreaks and/or effective response to control the spread of infectious diseases. The DS involves ongoing data collection, analysis, and dissemination of the analyzed information to healthcare authorities. 

The expert consultation conducted by Chatham House outlined seven principles to encourage equitable surveillance data sharing, including building trust, articulating the value, planning for data sharing, achieving quality data, understanding the legal context, creating data-sharing agreements, and monitoring and evaluation. These principles are the basis for comprehensive guidance with actionable recommendations for all stakeholders.

By sharing surveillance data, we can detect outbreaks earlier and respond more quickly, potentially reducing the spread of infectious diseases and saving lives. Sharing data also allows for a more coordinated response across borders, which can help to contain outbreaks before they become global epidemics.

Our Future Health: the UK’s most extensive ever health research program designed to help people live healthier lives for longer

The Our Future Health program aims to ensure healthier lives for people by discovering and testing more effective approaches to the prevention, early detection, and treatment of diseases.

Our Future Health is the UK’s largest-ever health research program, designed to become a world-leading health research resource and provide researchers with a vast pool of health data to identify more effective ways to tackle diseases. The program refers to analyzing significant data volumes to identify new signals that could help early detection of diseases. Additionally, it aims to find new ways to predict people at higher risk of medical conditions for effective prevention interventions. 

One of the most significant healthcare challenges leading to high healthcare costs and poorer outcomes is that the industry primarily focuses on treating diseases once they’ve already started to cause the symptom. By identifying new signals that indicate the onset of a disease, researchers could develop a treatment that would cause much less damage to the body. 

The key benefit of Our Future Health is it will enable more effective data sharing between healthcare providers, researchers, and patients. By sharing health data, researchers can better understand the health conditions that affect people in the UK and develop more effective interventions. 


The Value of Data Privacy

Interoperability in healthcare refers to seamless data exchange between different systems and accurately preserving the meaning inherent in the data by the system that produces the exchanged healthcare information.  

Privacy and security concerns are topping the list of healthcare data exchange barriers. In one of our recent articles, we discussed why healthcare data security solutions are important. We highly recommend you read this material if you need valuable insights and statistics on the importance of data security in healthcare. 

The industry sets severe penalties for healthcare data breaches and violations of HIPAA, GDPR, and other standards. Such violations create a loss of patient trust and even criminal penalties, for failure in regulatory compliance in healthcare can ruin an organization’s reputation. 

Lack of Interoperability Standards in Healthcare

Healthcare data standards provide unified rules for coding and messaging for correct interpretation and meaningful use of healthcare information. However, not many standards target the data exchange mechanism and ensure superb communication in different healthcare systems. 

Without interoperability standards, the industry would have to complete manual data entry to share the information, which is time-consuming, expensive, and error-prone. In addition, poor healthcare data interoperability can lead to data silos, complicating the exchange and further analysis of the exchanged information. 

The previous attempts to establish healthcare interoperability standards using the HL7 V3 standard provided a unified reference information model (RIM) but failed. In the FHIR VS. HL7 article, we shared the structure of HL7 V3 RIM and an FHIR Resource to see how complicated RIM was.  As well as RIM, FHIR Resources are modular. However, the FHIR data model allows for extensions and constraints so that FHIR implementers can use only relevant components to their needs. 

The complexity and high implementation cost made it challenging for smaller healthcare organizations to adopt the HL7 V3 standard, resulting in the FHIR standard’s further popularity.


Standardized data format

The lack of agreement on the standard representation, format, definition, structuring, transmission, and management can hinder health IT systems from exchanging health data efficiently. The FHIR standard ensures a modern web-based approach to exchanging health data. It was designed to address the limitations of previous HL7 standards. Healthcare stakeholders should pay attention to the importance of EHR interoperability and leverage the FHIR approach to health data management to achieve semantic interoperability in healthcare

One of the main advantages of the FHIR data format is that it is both human and machine-readable. Exchanging health information in FHIR format means the high speed, flexibility, and simplicity of the exchange process. FHIR uses a RESTful API approach and HTTP methods to manipulate the data. FHIR API simplifies HL7 integration and helps to comply with modern healthcare data standards.

The power of healthcare data is highly constrained when data is siloed and cannot be easily shared across different organizations. The standardized data format is the key to unlocking the power of health data.

Data governance and policies

In the healthcare sector, data governance manages data availability, security, and integrity across different systems and organizations. Data governance policies include strict rules and procedures for ensuring the security of sensitive patient data, which we mentioned earlier as a significant barrier to interoperability. 

Healthcare policies regulate how data is collected, stored, accessed, and shared across different entities. Clear policies and the risk of harsh penalties will constantly encourage healthcare stakeholders to comply with health data regulations and stay transparent about how they handle sensitive patient data. 


Despite the vast potential of healthcare data, it comes with the responsibility of preserving patient privacy. The International Medical Informatics Association (IMIA) recognizes the importance of ensuring the balance between health data access, usage, and sharing. As a result, it has dedicated one of its Yearbooks to exploring challenges associated with sharing health data.

The IMIAs Yearbook has emphasized the importance of patient perspectives in understanding how patients view their health data and how their privacy should be protected. Sticking to robust interoperability practices is one of the best ways to balance data sharing and privacy.

At Edenlab, we are committed to improving data sharing in healthcare and implementing the Fast Healthcare Interoperability Resources (FHIR) standard. For example, the Kodjin FHIR Server is designed to be scalable and easy to use, making it an ideal solution for healthcare providers and organizations of all sizes. 

Using the FHIR standard, we ensure data is exchanged in a standardized way, making it easier to share and analyze. By working together, we can unlock the power of health data and improve patient outcomes. If you want to learn more about the Kodjin Interoperability Suite and how it can help your organization, contact us today.

Post author

Iryna Biiovska

System Analyst at Edenlab

More article about Blog about Healthcare Data

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